Lake long-time-sequence continuous water level reconstruction method and system based on virtual station and medium

Abstract

The invention discloses a lake long-time-sequence continuous water level reconstruction method and system based on a virtual station and a medium, and belongs to the technical field of hydrological information. The method comprises the following steps: a data reading stage: accessing a cloud geographic information processing platform to obtain water body frequency data of a target lake; a potential virtual station construction stage: defining a potential virtual station according to the permanent water body boundary and the maximum water body boundary; different water level lake shoreline extraction stage: extracting the lake shorelines of the potential virtual station in different water level periods according to the water body frequency data; in the lake shoreline curvature index construction stage, the potential virtual station with the minimum lake shoreline curvature index serves as the optimal virtual station; a lake virtual station area-water level relation model construction stage; in the lake long-time-sequence continuous water level reconstruction stage, missing target lake water level observation data are estimated for lake long-time-sequence continuous water level reconstruction. Full-process automation is realized; only for areas with obvious lake changes, the model construction efficiency and the extraction precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of hydrological information technology, and in particular to a method, system and medium for reconstructing a long-time continuous water level of a lake based on a virtual station. Background Art

[0002] Research shows that between 1984 and 2015, the world’s permanent water bodies decreased by about 9 million tons. 4 km 2 , and about 1.84×10 5 km 2 New water bodies have been created, but the drivers of these significant changes have not been fully elucidated, partly because of the lack of long-term lake water level data, which limits the ability to sustainably manage surface water resources. Satellite remote sensing can evaluate changes in surface water resources by repeatedly observing lake water levels, but the different resolutions of altimetry satellites, long revisit cycles, and discontinuous service periods limit their acquisition of long-term continuous water level data. However, this problem can be improved by combining optical satellite data. Since the development of optical satellites precedes that of altimetry satellites, the Landsat series of satellites represented by Landsat5 have supported the reconstruction of lake water levels from 1984 to the present through long-term services. A recent study reconstructed the water level data of 1972 large lakes from 1992 to 2020 by constructing lake storage capacity curves. However, this study mainly focuses on large lakes, and the water level reconstruction period is relatively short. The monitoring of long-term lake water levels is the basis for studying the response of lake water levels to climate change. This method faces problems such as huge workload and inaccurate extraction of the entire lake area in practical applications, which affects the accuracy of water level reconstruction. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method, system and medium for reconstructing the long-term continuous water level of a lake based on a virtual station.

[0004] The object of the present invention is achieved through the following technical solutions: The first aspect of the present invention provides: a method for reconstructing a long-time continuous water level of a lake based on a virtual station, comprising the following steps: S1: Data reading phase, accessing the cloud-based geographic information processing platform to obtain water frequency data of the target lake; S2: In the potential virtual station construction stage, the boundary line of the area where the water body frequency is greater than or equal to the first preset value in the target lake is identified according to the water body frequency data as the permanent water body boundary of the target lake, and the boundary line of the area where the water body frequency is greater than or equal to the fifth preset value is identified as the maximum water body boundary of the target lake, and the potential virtual station is defined according to the permanent water body boundary and the maximum water body boundary; S3: different water level shoreline extraction stage, extract the shoreline of the potential virtual station in different water level periods according to the water body frequency data, and define the boundary line of the area with a water body frequency greater than or equal to the first preset value in the potential virtual station as the permanent water body shoreline, the boundary line of the area with a water body frequency greater than or equal to the second preset value as the dry season shoreline, the boundary line of the area with a water body frequency greater than or equal to the third preset value as the normal water season shoreline, the boundary line of the area with a water body frequency greater than or equal to the fourth preset value as the flood season shoreline, and the boundary line of the area with a water body frequency greater than or equal to the fifth preset value as the maximum water body shoreline; S4: The shoreline curvature index construction phase, the average value of the shoreline curvature of the potential virtual station in different water level periods is calculated to obtain the shoreline curvature index, and the potential virtual station with the smallest shoreline curvature index is taken as the best virtual station; S5: Lake virtual station area-water level relationship model construction stage, using the monthly water body data of the surface water dataset to extract the monthly water body range of the target lake during the study period, matching the monthly data of the target lake area and the target lake water level that existed simultaneously during the study period, and using the linear empirical relationship model to construct the lake virtual station area-water level relationship model; S6: During the long-term continuous water level reconstruction stage of the lake, when the paired data group of the target lake area and the target lake water level exceeds the sixth preset value, and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds the seventh preset value, the target lake data is retained, and the monthly area data of the target lake and the lake virtual station area-water level relationship model are used to calculate the missing target lake water level observation data for long-term continuous water level reconstruction of the lake.

[0005] Preferably, the accessing of the cloud-based geographic information processing platform to obtain the water frequency data of the target lake comprises the following steps: The cloud-based geographic information processing platform was accessed through the Python API interface. After retrieving the lake range of the specified ID and generating a buffer zone, the water body frequency dataset in the surface water dataset was clipped to obtain the water body frequency data of the target lake during the study period.

[0006] Preferably, the step of defining a potential virtual station according to a permanent water body boundary and a maximum water body boundary comprises the following steps: The permanent water body boundary is converted into a permanent water body polygon composed of straight line segments; then the outer boundary line of the permanent water body polygon is disconnected with the inflection points of the permanent water body polygon as the boundary, and the straight line segments with a length greater than a first preset length are screened out as alternative lakeshore segments; based on the alternative lakeshore segments, an circumscribed rectangle that can enclose the permanent water body boundary and the maximum water body boundary is constructed to obtain a potential virtual station.

[0007] Preferably, the calculation of the curvature of the lake shoreline at different water level periods of the potential virtual station comprises the following steps: Extract the lake shoreline of the potential virtual station at different water level periods; smooth the target lake shoreline to remove atypical jagged fluctuations; mark a mark point on the target lake shoreline every second preset length; calculate the azimuth between two adjacent mark points of the target lake shoreline; take the average of the absolute values ​​of all calculated azimuths as the curvature of the target lake shoreline; Finally, the curvature of the lakeshore in all water level periods of the potential virtual station was averaged to obtain the lakeshore curvature index.

[0008] Preferably, the cloud-based geographic information processing platform is a Google Earth Engine platform, and the surface water dataset is a Global Surface Water dataset.

[0009] Preferably, the first preset value is 95%, the second preset value is 75%, the third preset value is 50%, the fourth preset value is 25%, the fifth preset value is 1%, the sixth preset value is 10, and the seventh preset value is 0.6.

[0010] Preferably, the second preset length is 50 m.

[0011] The second aspect of the present invention provides: a system for reconstructing a long-time continuous water level of a lake based on a virtual station, which is used to implement any of the above-mentioned methods for reconstructing a long-time continuous water level of a lake based on a virtual station, comprising: A data reading module is used to access the cloud-based geographic information processing platform to obtain water frequency data of the target lake; A potential virtual station construction module is used to identify, based on the water body frequency data, a boundary line of an area in the target lake where the water body frequency is greater than or equal to a first preset value as the permanent water body boundary of the target lake, and a boundary line of an area where the water body frequency is greater than or equal to a fifth preset value as the maximum water body boundary of the target lake, and define a potential virtual station based on the permanent water body boundary and the maximum water body boundary; A lakeshore extraction module at different water levels is used to extract lakeshores at different water level periods of a potential virtual station according to water body frequency data, and to define the boundary line of an area in which the water body frequency in the potential virtual station is greater than or equal to a first preset value as a permanent water body lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a second preset value as a dry season lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a third preset value as a normal water season lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a fourth preset value as a flood season lakeshore, and the boundary line of an area in which the water body frequency is greater than or equal to a fifth preset value as a maximum water body lakeshore; The lake shoreline curvature index construction module is used to calculate the average curvature of the lake shoreline at different water level periods of the potential virtual station to obtain the lake shoreline curvature index, and the potential virtual station with the smallest lake shoreline curvature index is taken as the best virtual station; The lake virtual station area-water level relationship model construction module is used to extract the monthly water body range of the target lake during the study period using the monthly water body data of the surface water dataset, match the monthly data of the target lake area and the target lake water level that exist simultaneously during the study period, and use the linear empirical relationship model to construct the lake virtual station area-water level relationship model; The module for reconstructing the long-term continuous water level of lakes is used to retain the target lake data when the paired data group of the target lake area and the target lake water level exceeds the sixth preset value and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds the seventh preset value, and to reconstruct the long-term continuous water level of the lake by calculating the missing target lake water level observation data using the monthly area data of the target lake and the lake virtual station area-water level relationship model.

[0012] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned methods for reconstructing long-term continuous water levels of lakes based on virtual stations is implemented.

[0013] The beneficial effects of the present invention are: 1) The processing system built with Python programming language realizes the full process automation of data acquisition, virtual site selection, area extraction, water level data filling and other steps, which significantly improves work efficiency and saves data processing time.

[0014] 2) When constructing the lake area-water level relationship model, only the areas with significant lake changes are targeted, which improves the model construction efficiency and extraction accuracy.

[0015] 3) The “shoreline curvature index” was used to quantify the flatness of the lakeshore and select the best virtual station to improve the performance of the lake area-water level relationship model. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the method for reconstructing long-term continuous water level of lakes based on virtual stations. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0018] Virtual Station (Virtual Station) technology takes advantage of the large-scale, long-time series and automated observation of remote sensing satellites, and regularly extracts water information at the intersection of the satellite observation orbit and the research target, forming a hydrological observation station similar to an unmanned one, which is widely used in the study of river hydrological changes. Due to the high efficiency, convenience and accuracy of this technology in monitoring river hydrological changes, the present invention applies it to the time series reconstruction of lake hydrological changes. Through the time series change characteristics of lake water levels, it can effectively diagnose the hydrological conditions of lakes with different change types, such as expansion, shrinkage, relative stability and interannual fluctuation. The present invention constructs a lakeshore curvature index, develops an automated site selection process for virtual stations, improves the efficiency of constructing the lake virtual station area-water level relationship model, effectively fills and extends the lake water level observation time series, and makes it possible to analyze the characteristics of lake water level changes at multiple time scales (seasonal, interannual and interdecadal).

[0019] See also Figure 1 The first aspect of the present invention provides: a method for reconstructing a long-time continuous water level of a lake based on a virtual station, comprising the following steps: S1: Data reading phase, accessing the cloud-based geographic information processing platform to obtain water frequency data of the target lake; S2: In the potential virtual station construction stage, the boundary line of the area where the water body frequency is greater than or equal to the first preset value in the target lake is identified according to the water body frequency data as the permanent water body boundary of the target lake, and the boundary line of the area where the water body frequency is greater than or equal to the fifth preset value is identified as the maximum water body boundary of the target lake, and the potential virtual station is defined according to the permanent water body boundary and the maximum water body boundary; S3: different water level shoreline extraction stage, extract the shoreline of the potential virtual station in different water level periods according to the water body frequency data, and define the boundary line of the area with a water body frequency greater than or equal to the first preset value in the potential virtual station as the permanent water body shoreline, the boundary line of the area with a water body frequency greater than or equal to the second preset value as the dry season shoreline, the boundary line of the area with a water body frequency greater than or equal to the third preset value as the normal water season shoreline, the boundary line of the area with a water body frequency greater than or equal to the fourth preset value as the flood season shoreline, and the boundary line of the area with a water body frequency greater than or equal to the fifth preset value as the maximum water body shoreline; S4: The shoreline curvature index construction phase, the average value of the shoreline curvature of the potential virtual station in different water level periods is calculated to obtain the shoreline curvature index, and the potential virtual station with the smallest shoreline curvature index is taken as the best virtual station; S5: Lake virtual station area-water level relationship model construction stage, using the monthly water body data of the surface water dataset to extract the monthly water body range of the target lake during the study period, matching the monthly data of the target lake area and the target lake water level that existed simultaneously during the study period, and using the linear empirical relationship model to construct the lake virtual station area-water level relationship model; S6: During the long-term continuous water level reconstruction stage of the lake, when the paired data group of the target lake area and the target lake water level exceeds the sixth preset value, and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds the seventh preset value, the target lake data is retained, and the monthly area data of the target lake and the lake virtual station area-water level relationship model are used to calculate the missing target lake water level observation data for long-term continuous water level reconstruction of the lake.

[0020] In the present embodiment, the first preset value, the second preset value, the third preset value, the fourth preset value, the fifth preset value, the sixth preset value, and the seventh preset value are values ​​pre-set according to the actual working environment, and are not limited to a specific value or range of values, as long as the purpose of the present invention can be achieved. The monitoring focus is narrowed from the entire lake to the lakeshore virtual station area with significant changes, which greatly reduces the workload while accurately capturing the hydrological changes of the lake. Therefore, ensuring the scientific nature of the selection of the virtual station location is the key to the long-term continuous water level reconstruction technology of the lake using virtual station technology. By constructing a lakeshore curvature index to quantify the flatness of the lakeshore, developing an automated site selection process for virtual stations, and improving the efficiency of constructing the area-water level relationship model of the lake virtual station, long-term continuous water level reconstruction of the lake can be achieved when ground data is missing. The lake virtual station area-water level relationship model constructed by the linear empirical relationship model is used to draw the changing relationship between the lake water level and area.

[0021] In some embodiments, the accessing of the cloud-based geographic information processing platform to obtain the water frequency data of the target lake includes the following steps: The cloud-based geographic information processing platform was accessed through the Python API interface. After retrieving the lake range of the specified ID and generating a buffer zone, the water body frequency dataset in the surface water dataset was clipped to obtain the water body frequency data of the target lake during the study period.

[0022] In this embodiment, the water body frequency data can be converted into water area range to reveal the historical change profile of the water body in the target lake.

[0023] In some embodiments, the step of defining a potential virtual station according to a permanent water body boundary and a maximum water body boundary comprises the following steps: The permanent water body boundary is converted into a permanent water body polygon composed of straight line segments; then the outer boundary line of the permanent water body polygon is disconnected with the inflection points of the permanent water body polygon as the boundary, and the straight line segments with a length greater than a first preset length are screened out as alternative lakeshore segments; based on the alternative lakeshore segments, an circumscribed rectangle that can enclose the permanent water body boundary and the maximum water body boundary is constructed to obtain a potential virtual station.

[0024] In this embodiment, the first preset length is a length preset according to the actual working environment, and is not limited to a specific length or length range, as long as the purpose of the present invention can be achieved. First, the permanent water body boundary is converted into a permanent water body polygon composed of straight line segments to simplify the permanent water body boundary, and then after being disconnected by inflection points, several (3-5 or more) relatively long (greater than the first preset length) straight lines are selected as candidate lakeshore segments for constructing potential virtual stations.

[0025] In some embodiments, the method of calculating the curvature of the lakeshore at different water level periods of the potential virtual station includes the following steps: Extract the lake shoreline of the potential virtual station at different water level periods; smooth the target lake shoreline to remove atypical jagged fluctuations; mark a mark point on the target lake shoreline every second preset length; calculate the azimuth between two adjacent mark points of the target lake shoreline; take the average of the absolute values ​​of all calculated azimuths as the curvature of the target lake shoreline; Finally, the curvature of the lakeshore in all water level periods of the potential virtual station was averaged to obtain the lakeshore curvature index.

[0026] In this embodiment, the first preset length is a length pre-set according to the actual working environment, and is not limited to a specific length or length range, as long as the purpose of the present invention can be achieved. The lakeshores in different water level periods include the permanent water body lakeshore of the potential virtual station, the dry season lakeshore, the level water season lakeshore, the flood season lakeshore and the maximum water body lakeshore. These five types of lakeshores respectively show the changes in the water area of ​​the lake in each hydrological period. The smaller the value of the lakeshore curvature index, the flatter the lakeshore, which can more accurately reflect the changes in the lake.

[0027] In some embodiments, the cloud-based geographic information processing platform is a Google Earth Engine (GEE) platform, and the surface water dataset is a Global Surface Water (GSW) dataset.

[0028] In some embodiments, the first preset value is 95%, the second preset value is 75%, the third preset value is 50%, the fourth preset value is 25%, the fifth preset value is 1%, the sixth preset value is 10, and the seventh preset value is 0.6.

[0029] In this embodiment, when the number of paired data sets of target lake area and target lake water level exceeds 10 and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds 0.6, the lake virtual station area-water level relationship model is considered to have sufficient stability and the corresponding lake data can be retained for data reconstruction.

[0030] In some embodiments, the second preset length is 50 m.

[0031] The second aspect of the present invention provides: a system for reconstructing a long-time continuous water level of a lake based on a virtual station, which is used to implement any of the above-mentioned methods for reconstructing a long-time continuous water level of a lake based on a virtual station, comprising: A data reading module is used to access the cloud-based geographic information processing platform to obtain water frequency data of the target lake; A potential virtual station construction module is used to identify, based on the water body frequency data, a boundary line of an area in the target lake where the water body frequency is greater than or equal to a first preset value as the permanent water body boundary of the target lake, and a boundary line of an area where the water body frequency is greater than or equal to a fifth preset value as the maximum water body boundary of the target lake, and define a potential virtual station based on the permanent water body boundary and the maximum water body boundary; A lakeshore extraction module at different water levels is used to extract lakeshores at different water level periods of a potential virtual station according to water body frequency data, and to define the boundary line of an area in which the water body frequency in the potential virtual station is greater than or equal to a first preset value as a permanent water body lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a second preset value as a dry season lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a third preset value as a normal water season lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a fourth preset value as a flood season lakeshore, and the boundary line of an area in which the water body frequency is greater than or equal to a fifth preset value as a maximum water body lakeshore; The lake shoreline curvature index construction module is used to calculate the average curvature of the lake shoreline at different water level periods of the potential virtual station to obtain the lake shoreline curvature index, and the potential virtual station with the smallest lake shoreline curvature index is taken as the best virtual station; The lake virtual station area-water level relationship model construction module is used to extract the monthly water body range of the target lake during the study period using the monthly water body data of the surface water dataset, match the monthly data of the target lake area and the target lake water level that exist simultaneously during the study period, and use the linear empirical relationship model to construct the lake virtual station area-water level relationship model; The module for reconstructing the long-term continuous water level of lakes is used to retain the target lake data when the paired data group of the target lake area and the target lake water level exceeds the sixth preset value and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds the seventh preset value, and to reconstruct the long-term continuous water level of the lake by calculating the missing target lake water level observation data using the monthly area data of the target lake and the lake virtual station area-water level relationship model.

[0032] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned methods for reconstructing long-term continuous water levels of lakes based on virtual stations is implemented.

[0033] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for reconstructing lake long-term continuous water level based on virtual stations, characterized by: The following steps are involved: S1: Data reading phase, accessing the cloud-based geographic information processing platform to obtain water frequency data of the target lake; S2: In the potential virtual station construction stage, the boundary line of the area where the water body frequency is greater than or equal to the first preset value in the target lake is identified according to the water body frequency data as the permanent water body boundary of the target lake, and the boundary line of the area where the water body frequency is greater than or equal to the fifth preset value is identified as the maximum water body boundary of the target lake, and the potential virtual station is defined according to the permanent water body boundary and the maximum water body boundary; S3: different water level shoreline extraction stage, according to the water body frequency data, the shoreline of the potential virtual station in different water level periods is extracted, and the boundary line of the area with a water body frequency greater than or equal to the first preset value in the potential virtual station is regarded as the permanent water body shoreline, the boundary line of the area with a water body frequency greater than or equal to the second preset value is regarded as the dry season shoreline, the boundary line of the area with a water body frequency greater than or equal to the third preset value is regarded as the normal water season shoreline, the boundary line of the area with a water body frequency greater than or equal to the fourth preset value is regarded as the flood season shoreline, and the boundary line of the area with a water body frequency greater than or equal to the fifth preset value is regarded as the maximum water body shoreline; S4: The shoreline curvature index construction phase, the average value of the shoreline curvature of the potential virtual station in different water level periods is calculated to obtain the shoreline curvature index, and the potential virtual station with the smallest shoreline curvature index is taken as the best virtual station; S5: Lake virtual station area-water level relationship model construction stage, using the monthly water body data of the surface water dataset to extract the monthly water body range of the target lake during the study period, matching the monthly data of the target lake area and the target lake water level that existed simultaneously during the study period, and using the linear empirical relationship model to construct the lake virtual station area-water level relationship model; S6: During the long-term continuous water level reconstruction stage of the lake, when the paired data group of the target lake area and the target lake water level exceeds the sixth preset value, and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds the seventh preset value, the target lake data is retained, and the monthly area data of the target lake and the lake virtual station area-water level relationship model are used to calculate the missing target lake water level observation data for long-term continuous water level reconstruction of the lake.

2. The method for reconstructing lake long-time continuous water level based on virtual stations according to claim 1 is characterized in that: The accessing of the cloud-based geographic information processing platform to obtain the water frequency data of the target lake includes the following steps: The cloud-based geographic information processing platform was accessed through the Python API interface. After retrieving the lake range of the specified ID and generating a buffer zone, the water body frequency dataset in the surface water dataset was clipped to obtain the water body frequency data of the target lake during the study period.

3. The method for reconstructing lake long-term continuous water level based on virtual stations according to claim 1 is characterized in that: The method of defining a potential virtual station according to a permanent water body boundary and a maximum water body boundary comprises the following steps: The permanent water body boundary is converted into a permanent water body polygon composed of straight line segments; then the outer boundary line of the permanent water body polygon is disconnected with the inflection points of the permanent water body polygon as the boundary, and the straight line segments with a length greater than a first preset length are screened out as alternative lakeshore segments; based on the alternative lakeshore segments, an circumscribed rectangle that can enclose the permanent water body boundary and the maximum water body boundary is constructed to obtain a potential virtual station.

4. The method for reconstructing lake long-time continuous water level based on virtual stations according to claim 1 is characterized in that: The calculation of the curvature of the lake shoreline at different water level periods of the potential virtual station comprises the following steps: Extract the lake shoreline of the potential virtual station at different water level periods; smooth the target lake shoreline to remove atypical jagged fluctuations; mark a mark point on the target lake shoreline every second preset length; calculate the azimuth between two adjacent mark points of the target lake shoreline; take the average of the absolute values ​​of all calculated azimuths as the curvature of the target lake shoreline; Finally, the curvature of the lakeshore in all water level periods of the potential virtual station was averaged to obtain the lakeshore curvature index.

5. The method for reconstructing lake long-time continuous water level based on virtual stations according to any one of claims 1 to 4, characterized in that: The cloud-based geographic information processing platform is the Google Earth Engine platform, and the surface water dataset is the Global Surface Water dataset.

6. The method for reconstructing the long-term continuous water level of a lake based on a virtual station according to any one of claims 1 to 4, characterized in that: The first preset value is 95%, the second preset value is 75%, the third preset value is 50%, the fourth preset value is 25%, the fifth preset value is 1%, the sixth preset value is 10, and the seventh preset value is 0.

6.

7. The method for reconstructing lake long-term continuous water level based on virtual stations according to claim 4 is characterized in that: The second preset length is 50 m.

8. A lake long-term continuous water level reconstruction system based on virtual stations, characterized by: The method for reconstructing the long-time continuous water level of a lake based on a virtual station as claimed in any one of claims 1 to 7 comprises: A data reading module is used to access the cloud-based geographic information processing platform to obtain water frequency data of the target lake; A potential virtual station construction module is used to identify, based on the water body frequency data, a boundary line of an area in the target lake where the water body frequency is greater than or equal to a first preset value as the permanent water body boundary of the target lake, and a boundary line of an area where the water body frequency is greater than or equal to a fifth preset value as the maximum water body boundary of the target lake, and define a potential virtual station based on the permanent water body boundary and the maximum water body boundary; A lakeshore extraction module at different water levels is used to extract lakeshores at different water level periods of a potential virtual station according to water body frequency data, and to define the boundary line of an area in which the water body frequency in the potential virtual station is greater than or equal to a first preset value as a permanent water body lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a second preset value as a dry season lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a third preset value as a normal water season lakeshore, the boundary line of an area in which the water body frequency is greater than or equal to a fourth preset value as a flood season lakeshore, and the boundary line of an area in which the water body frequency is greater than or equal to a fifth preset value as a maximum water body lakeshore; The lake shoreline curvature index construction module is used to calculate the average curvature of the lake shoreline at different water level periods of the potential virtual station to obtain the lake shoreline curvature index, and the potential virtual station with the smallest lake shoreline curvature index is taken as the best virtual station; The lake virtual station area-water level relationship model construction module is used to extract the monthly water body range of the target lake during the study period using the monthly water body data of the surface water dataset, match the monthly data of the target lake area and the target lake water level that exist simultaneously during the study period, and use the linear empirical relationship model to construct the lake virtual station area-water level relationship model; The module for reconstructing the long-term continuous water level of lakes is used to retain the target lake data when the paired data group of the target lake area and the target lake water level exceeds the sixth preset value and the model correlation coefficient r² of the lake virtual station area-water level relationship model exceeds the seventh preset value, and to reconstruct the long-term continuous water level of the lake by calculating the missing target lake water level observation data using the monthly area data of the target lake and the lake virtual station area-water level relationship model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are loaded and executed by the processor, the method for reconstructing the long-time continuous water level of a lake based on a virtual station as described in any one of claims 1 to 7 is implemented.

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